{"id":991469,"date":"2026-08-17T20:35:53","date_gmt":"2026-08-17T13:35:53","guid":{"rendered":"https:\/\/5id.vn\/parameter-efficient-fine-tuning-5-llm-methods\/"},"modified":"2026-08-17T20:35:53","modified_gmt":"2026-08-17T13:35:53","slug":"parameter-efficient-fine-tuning-5-llm-methods","status":"publish","type":"post","link":"https:\/\/5id.vn\/vi\/parameter-efficient-fine-tuning-5-llm-methods\/","title":{"rendered":"Parameter Efficient Fine Tuning: 5 LLM Methods"},"content":{"rendered":"<div class=\"vgblk-rw-wrapper limit-wrapper\">\n<div class=\"n8n-seo-article\" style=\"max-width:100%;line-height:1.7\">\n<p>&quot;To fine-tune a 70B model, you don&#039;t need to train all 70 billion parameters.&quot; This statement might sound counterintuitive, yet it perfectly encapsulates the current paradigm for fine-tuning large language models (LLMs). This revolutionary approach is known as <strong>Parameter-Efficient Fine-Tuning<\/strong> (PEFT), and it&#039;s transforming how developers and researchers interact with massive AI models.<\/p>\n<p>Imagine fine-tuning an LLM as updating a comprehensive book. The evolution of fine-tuning methods reflects a clear progression towards efficiency:<\/p>\n<ul>\n<li><strong>Full Fine-tuning:<\/strong> This is akin to rewriting the entire book from scratch, adjusting every single word and sentence. While thorough, it&#039;s resource-intensive.<\/li>\n<li><strong>LoRA (Low-Rank Adaptation):<\/strong> Instead of rewriting, LoRA keeps the original book intact and only adds a set of \u201csmart notes\u201d or small, trainable matrices (adapters) to guide the model&#039;s behavior.<\/li>\n<li><strong>LoRA-FA (LoRA with Frozen Adapters):<\/strong> Building on LoRA, this method further freezes a portion of the adapter parameters, reducing the number of parameters that need to be learned even more.<\/li>\n<li><strong>QLoRA (Quantized LoRA):<\/strong> This technique drastically reduces memory requirements by storing the original model in a highly compressed 4-bit quantized format, making large models accessible on more modest hardware.<\/li>\n<li><strong>TinyLoRA:<\/strong> Pushing efficiency to its extreme, TinyLoRA learns only a very small vector, sometimes involving just a few parameters in certain cases, offering ultra-lightweight adaptation.<\/li>\n<\/ul>\n<p>This progression reveals a clear trend in AI development: from training the entire model, to learning only the necessary changes, then compressing the model, and finally, retaining only what is truly essential. This paradigm shift underscores a critical insight: major advancements in AI don&#039;t always come from making models larger. Often, the more impactful breakthroughs stem from training less while still achieving nearly equivalent performance. This is the core strength and practical value of <strong>Parameter-Efficient Fine-Tuning<\/strong>.<\/p>\n<h2>References<\/h2>\n<p>These external sources were used to verify the article and provide deeper context.<\/p>\n<ul class=\"reference-list\" style=\"padding-left:0;margin:16px 0 28px 0\">\n<li style=\"margin:0 0 12px 0\"><a href=\"https:\/\/www.linkedin.com\/posts\/diyabhowmick_what-if-i-told-you-you-dont-need-to-train-share-7487910386063761409-n_5r\/?utm_source=social_share_send&amp;utm_medium=member_desktop_web&amp;rcm=ACoAAB8zGzgBCs7tk97g0kh_mpAfaczB4jwFYeo\" target=\"_blank\" rel=\"noopener\" style=\"display:block;padding:14px 16px;border:1px solid #c9d7ee;border-radius:8px;background:#f7faff;color:#07104a;text-decoration:none\"><span style=\"display:block;margin-bottom:5px;font-size:12px;line-height:1.4;color:#52627a;text-transform:uppercase;letter-spacing:0;font-weight:700\">Source: LinkedIn<\/span><strong style=\"display:block;margin-bottom:6px;color:#07104a;text-decoration:underline;font-size:17px;line-height:1.45\">diyabhowmick what if i told you you dont need to train share 7487910386063761409 n 5r &#8211; LinkedIn<\/strong><span style=\"display:block;color:#3b4a62;font-size:14px;line-height:1.45\">Open original resource<\/span><\/a><\/li>\n<\/ul>\n<h2>Source Images<\/h2>\n<div class=\"lark-source-gallery\" style=\"display:grid;grid-template-columns:repeat(auto-fit,minmax(220px,1fr));gap:16px;margin:18px 0 28px 0\">\n<figure style=\"margin:0\"><img decoding=\"async\" src=\"https:\/\/5id.vn\/wp-content\/uploads\/2026\/08\/lark-body-om_x100b6704afea18a4e2b9e424125aa70-1-1786973751664.jpg\" alt=\"Parameter Efficient Fine Tuning: 5 LLM Methods - image 1\" loading=\"lazy\" style=\"display:block;width:100%;max-width:100%;height:auto;border-radius:8px\" \/><\/figure>\n<\/div>\n<h2>Conclusion<\/h2>\n<p>The evolution of fine-tuning techniques, particularly <strong>Parameter-Efficient Fine-Tuning<\/strong>, demonstrates a powerful shift towards smarter, more resource-efficient AI development. By focusing on adapting only a fraction of a model&#039;s parameters, we can achieve significant performance gains without the prohibitive costs of full retraining.<\/p>\n<\/div>\n<\/div>\n<p><!-- .vgblk-rw-wrapper --><\/p>","protected":false},"excerpt":{"rendered":"<p>Discover how Parameter Efficient Fine Tuning allows you to update large language models like 70B without training all parameters, saving resources<\/p>","protected":false},"author":26,"featured_media":991468,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_focuskw":"Parameter Efficient Fine Tuning","_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"Discover how Parameter Efficient Fine Tuning allows you to update large language models like 70B without training all parameters, saving resources","_yoast_wpseo_linkdex":"90","_yoast_wpseo_content_score":"90","rank_math_focus_keyword":"","rank_math_description":"","rank_math_title":"","source_url":"https:\/\/www.linkedin.com\/posts\/diyabhowmick_what-if-i-told-you-you-dont-need-to-train-share-7487910386063761409-n_5r\/?utm_source=social_share_send&utm_medium=member_desktop_web&rcm=ACoAAB8zGzgBCs7tk97g0kh_mpAfaczB4jwFYeo","source_name":"diyabhowmick what if i told you you dont need to train share 7487910386063761409 n 5r - LinkedIn","footnotes":""},"categories":[17,11],"tags":[],"class_list":["post-991469","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-development","category-news"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Parameter Efficient Fine Tuning: 5 LLM Methods - 5ID<\/title>\n<meta name=\"description\" content=\"Discover how Parameter Efficient Fine Tuning allows you to update large language models like 70B without training all parameters, saving resources\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/5id.vn\/vi\/parameter-efficient-fine-tuning-5-llm-methods\/\" \/>\n<meta property=\"og:locale\" content=\"vi_VN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Parameter Efficient Fine Tuning: 5 LLM Methods\" \/>\n<meta property=\"og:description\" content=\"Discover how Parameter Efficient Fine Tuning allows you to update large language models like 70B without training all parameters, saving resources\" \/>\n<meta property=\"og:url\" content=\"https:\/\/5id.vn\/vi\/parameter-efficient-fine-tuning-5-llm-methods\/\" \/>\n<meta property=\"og:site_name\" content=\"5ID\" \/>\n<meta property=\"article:published_time\" content=\"2026-08-17T13:35:53+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/5id.vn\/wp-content\/uploads\/2026\/08\/cover-5id-1786973752950-0.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"1200\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"5id\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u0110\u01b0\u1ee3c vi\u1ebft b\u1edfi\" \/>\n\t<meta name=\"twitter:data1\" content=\"5id\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u01af\u1edbc t\u00ednh th\u1eddi gian \u0111\u1ecdc\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 ph\u00fat\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/5id.vn\\\/parameter-efficient-fine-tuning-5-llm-methods\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/5id.vn\\\/parameter-efficient-fine-tuning-5-llm-methods\\\/\"},\"author\":{\"name\":\"5id\",\"@id\":\"https:\\\/\\\/5id.vn\\\/#\\\/schema\\\/person\\\/94846fcb6eafeca1d0e9fda167b6d880\"},\"headline\":\"Parameter Efficient Fine Tuning: 5 LLM Methods\",\"datePublished\":\"2026-08-17T13:35:53+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/5id.vn\\\/parameter-efficient-fine-tuning-5-llm-methods\\\/\"},\"wordCount\":378,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/5id.vn\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/5id.vn\\\/parameter-efficient-fine-tuning-5-llm-methods\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/5id.vn\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/cover-5id-1786973752950-0.jpg\",\"articleSection\":[\"Development\",\"News\"],\"inLanguage\":\"vi\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/5id.vn\\\/parameter-efficient-fine-tuning-5-llm-methods\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/5id.vn\\\/parameter-efficient-fine-tuning-5-llm-methods\\\/\",\"url\":\"https:\\\/\\\/5id.vn\\\/parameter-efficient-fine-tuning-5-llm-methods\\\/\",\"name\":\"Parameter Efficient Fine Tuning: 5 LLM Methods - 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